SELF-TUNING CONTROL OF STOCHASTIC EXTREMAL PLANTS

Abstract Self-tuning control with recursive identification of extremal objects is considered. The objects can be represented by combinations of linear dynamic and extremal static elements, and their outputs are disturbed by a colour noise with a general fractional-rational spectral density. Minimum-variance controllers for Wiener-Hammerstein-, Wiener-, and Hammerstein-type objects are designed. The estimates of unknown parameters in the equations of the minimum-variance controllers are obtained in the identification process of the controlled object in a closed loop. The efficiency of self-tuning control algorithms is illustrated by statistical simulation. On the basis of the methods worked out adaptive systems for optimization of fuel combustion and steam condensation processes in 300 MW thermal power units are developed.